Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions
Paper i proceeding, 2018
Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applica-tions to link virtual to real worlds. Practical visual localization approaches need to be robust to a wide variety of viewing condition, including day-night changes, as well as weather and seasonal variations, while providing highly accurate 6 degree-of-freedom (6DOF) camera pose estimates.
In this paper, we introduce the first benchmark datasets specifically designed for analyzing the impact of such factors on visual localization. Using carefully created ground truth poses for query images taken under a wide variety of conditions, we evaluate the impact of various factors on 6DOF camera pose estimation accuracy through extensive experiments with state-of-the-art localization approaches.
Based on our results, we draw conclusions about the difficulty of different conditions, showing that long-term localization is far from solved, and propose promising avenues for future work, including sequence-based localization approaches and the need for better local features. Our benchmark is available at visuallocalization.net
In this paper, we introduce the first benchmark datasets specifically designed for analyzing the impact of such factors on visual localization. Using carefully created ground truth poses for query images taken under a wide variety of conditions, we evaluate the impact of various factors on 6DOF camera pose estimation accuracy through extensive experiments with state-of-the-art localization approaches.
Based on our results, we draw conclusions about the difficulty of different conditions, showing that long-term localization is far from solved, and propose promising avenues for future work, including sequence-based localization approaches and the need for better local features. Our benchmark is available at visuallocalization.net
Visual localization
camera pose estimation
long term localization
benchmark
Författare
Torsten Sattler
Eidgenössische Technische Hochschule Zürich (ETH)
Will Maddern
University of Oxford
Carl Toft
Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik
Akihiko Torii
Tokyo Institute of Technology
Lars Hammarstrand
Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik
Erik Stenborg
Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik
Daniel Safari
Tokyo Institute of Technology
Danmarks Tekniske Universitet (DTU)
Masatoshi Okutomi
Tokyo Institute of Technology
Marc Pollefeys
Microsoft Corporation
Eidgenössische Technische Hochschule Zürich (ETH)
Josef Sivic
Ceske Vysoke Uceni Technicke v Praze
Institut National de Recherche en Informatique et en Automatique (INRIA)
Fredrik Kahl
Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik
Lunds universitet
Tomas Pajdla
Ceske Vysoke Uceni Technicke v Praze
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
10636919 (ISSN)
8601-8610 8578995978-153866420-9 (ISBN)
Conference on Computer Vision and Pattern Recognition 2018
Salt Lake City, USA,
Salt Lake City, USA,
COPPLAR CampusShuttle cooperative perception & planning platform
VINNOVA (2015-04849), 2016-01-01 -- 2018-12-31.
Styrkeområden
Informations- och kommunikationsteknik
Ämneskategorier
Robotteknik och automation
Sannolikhetsteori och statistik
Datorseende och robotik (autonoma system)
DOI
10.1109/CVPR.2018.00897